Results 21 to 30 of about 7,343,069 (347)
Loopy belief propagation and probabilistic image processing [PDF]
, 2003 Estimation of hyperparameters by maximization of the marginal likelihood in probabilistic image processing is investigated by using the cluster variation method.
Inoue, J., Tanaka, K., Titterington, M.
core +8 more sources
Teaching Probabilistic Graphical Models with OpenMarkov
Mathematics, 2022 OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries.
Francisco Javier Díez +3 more
doaj +1 more source
IEEE Transactions on Geoscience and Remote Sensing, 2022 Deep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training.
Martina Pastorino +3 more
semanticscholar +1 more source
On a hypergraph probabilistic graphical model [PDF]
Annals of Mathematics and Artificial Intelligence, 2020 We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more ...
Mohammad Ali Javidian +3 more
openaire +4 more sources
Multi-Marginal Optimal Transport and Probabilistic Graphical Models [PDF]
IEEE Transactions on Information Theory, 2020 We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph structure.
Isabel Haasler +4 more
semanticscholar +1 more source
Strengthening Probabilistic Graphical Models: The Purge-and-Merge Algorithm
IEEE Access, 2021 Probabilistic graphical models (PGMs) are powerful tools for solving systems of complex relationships over a variety of probability distributions. However, while tree-structured PGMs always result in efficient and exact solutions, inference on graph (or ...
Simon Streicher, Johan A. Du Preez
doaj +1 more source
The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism
Entropy, 2021 Causal inference methods based on conditional independence construct Markov equivalent graphs and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be ...
Nataliya Sokolovska +1 more
doaj +1 more source
Improved Local Search with Momentum for Bayesian Networks Structure Learning
Entropy, 2021 Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex ...
Xiaohan Liu +3 more
doaj +1 more source
Frontiers in Computer Science, 2020 Bayesian probabilistic modeling is supported by powerful computational tools like probabilistic programming and efficient Markov Chain Monte Carlo (MCMC) sampling. However, the results of Bayesian inference are challenging for users to interpret in tasks
Evdoxia Taka +2 more
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Inference in Probabilistic Graphical Models by Graph Neural Networks [PDF]
Asilomar Conference on Signals, Systems and Computers, 2018 A fundamental computation for statistical inference and accurate decision-making is to estimate the marginal probabilities or most probable states of task-relevant variables.
Kijung Yoon +7 more
semanticscholar +1 more source